ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)最新文献

{"title":"Effect of Recommendations on Serving Content with Unknown Demand","authors":"Samarth Gupta, Sharayu Moharir","doi":"10.1145/3289324","DOIUrl":"https://doi.org/10.1145/3289324","url":null,"abstract":"We consider the task of content replication in distributed content delivery systems used by Video-on-Demand (VoD) services with large content catalogs. The prior work in this area focuses on the setting where each request is generated independent of all past requests. Motivated by the fact that most popular VoD services offer recommendations to users based on their viewing history, in a departure from existing studies, we study the setting with time-correlation in requests coming from each user. We use a Markovian process to model each user’s request process. In addition to introducing time-correlation in user requests, our model is consistent with empirically observed properties of the request process for VoD services with recommendation engines. In the setting where the underlying Markov Chain is unknown and has to be learned from the very requests the system is trying to serve, we show that separating the task of estimating content popularity and using the estimates to design a static content replication policy is strictly sub-optimal. To prove this, we show that an adaptive policy, which jointly performs the task of estimation and content replication, outperforms all policies that separate the task of estimation and content replication.","PeriodicalId":105474,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114079731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Traffic Load-Balancing via Incremental Expansion of Routing Choices","authors":"Ping Yin, Sen Yang, Jun Xu, J. Dai, Bill Lin","doi":"10.1145/3243173","DOIUrl":"https://doi.org/10.1145/3243173","url":null,"abstract":"Routing policies play a major role in the performance of communication networks. Backpressure-based adaptive routing algorithms where traffic is load balanced along different routing paths on a per-packet basis have been studied extensively in the literature. Although backpressure-based algorithms have been shown to be networkwide throughput optimal, they typically have poor delay performance under light or moderate loads, because packets may be sent over unnecessarily long routes. Further, backpressure-based algorithms have required every node to compute differential backlogs for every per-destination queue with the corresponding per-destination queue at every adjacent node, which is expensive given the large number of possible pairwise differential backlogs between neighbor nodes. In this article, we propose new backpressure-based adaptive routing algorithms that only use shortest-path routes to destinations when they are sufficient to accommodate the given traffic load, but the proposed algorithms will incrementally expand routing choices as needed to accommodate increasing traffic loads. We show analytically by means of fluid analysis that the proposed algorithms retain networkwide throughput optimality, and we show empirically by means of simulations that our proposed algorithms provide substantial improvements in delay performance. Our evaluations further show that, in practice, our approach dramatically reduces the number of pairwise differential backlogs that have to be computed and the amount of corresponding backlog information that has to be exchanged, because routing choices are only incrementally expanded as needed.","PeriodicalId":105474,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114894075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Quantitative and Comparative Study of Network-Level Efficiency for Cloud Storage Services","authors":"Zhenhua Li, Yongfeng Zhang, Yunhao Liu, Tianyin Xu, Ennan Zhai, Yao Liu, Xiaobo Ma, Zhenyu Li","doi":"10.1145/3274526","DOIUrl":"https://doi.org/10.1145/3274526","url":null,"abstract":"Cloud storage services such as Dropbox and OneDrive provide users with a convenient and reliable way to store and share data from anywhere, on any device, and at any time. Their cornerstone is the data synchronization (sync) operation, which automatically maps the changes in users’ local file systems to the cloud via a series of network communications in a timely manner. Without careful design and implementation, however, the data sync mechanisms could generate overwhelming traffic, causing tremendous financial overhead and performance penalties to both service providers and end users. This article addresses a simple yet critical question: Is the current data sync traffic of cloud storage services efficiently used? We first define a novel metric TUE to quantify the Traffic Usage Efficiency of data synchronization. Then, by conducting comprehensive benchmark experiments and reverse engineering the data sync processes of eight widely used cloud storage services, we uncover their manifold practical endeavors for optimizing the TUE, including three intra-file approaches (compression, incremental sync, and interrupted transfer resumption), two cross-file/-user approaches (i.e., deduplication and peer-assisted offloading), two batching approaches (file bundling and sync deferment), and two web-specific approaches (thumbnail views and dynamic content loading). Our measurement results reveal that a considerable portion of the data sync traffic is, in a sense, wasteful and can be effectively avoided or significantly reduced via carefully designed data sync mechanisms. Most importantly, our study not only offers practical, actionable guidance for providers to build more efficient, traffic-economic services, but also helps end users pick appropriate services that best fit their use cases and budgets.","PeriodicalId":105474,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115026958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scalable Application- and User-aware Resource Allocation in Enterprise Networks Using End-Host Pacing","authors":"Christian Sieber, Susanna Schwarzmann, Andreas Blenk, T. Zinner, W. Kellerer","doi":"10.1145/3381996","DOIUrl":"https://doi.org/10.1145/3381996","url":null,"abstract":"Providing scalable user- and application-aware resource allocation for heterogeneous applications sharing an enterprise network is still an unresolved problem. The main challenges are as follows: (i) How do we define user- and application-aware shares of resources? (ii) How do we determine an allocation of shares of network resources to applications? (iii) How do we allocate the shares per application in heterogeneous networks at scale? In this article, we propose solutions to the three challenges and introduce a system design for enterprise deployment. Defining the necessary resource shares per application is hard, as the intended use case, the user’s environment, e.g., big or small display, and the user’s preferences influence the resource demand. We tackle the challenge by associating application flows with utility functions from subjective user experience models, selected Key Performance Indicators, and measurements. The specific utility functions then enable a mapping of network resources in terms of throughput and latency budget to a common user-level utility scale. A sensible distribution of the resources is determined by formulating a multi-objective mixed integer linear program to solve the throughput- and delay-aware embedding of each utility function in the network for a max-min fairness criteria. The allocation of resources in traditional networks with policing and scheduling cannot distinguish large numbers of classes and interacts badly with congestion control algorithms. We propose a resource allocation system design for enterprise networks based on Software-Defined Networking principles to achieve delay-constrained routing in the network and application pacing at the end-hosts. The system design is evaluated against best effort networks in a proof-of-concept set-up for scenarios with increasing number of parallel applications competing for the throughput of a constrained link. The competing applications belong to the five application classes web browsing, file download, remote terminal work, video streaming, and Voice-over-IP. The results show that the proposed methodology improves the minimum and total utility, minimizes packet loss and queuing delay at bottlenecks, establishes fairness in terms of utility between applications, and achieves predictable application performance at high link utilization.","PeriodicalId":105474,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123285216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensuring Persistent Content in Opportunistic Networks via Stochastic Stability Analysis","authors":"Ljubica Pajevic, V. Fodor, G. Karlsson","doi":"10.1145/3232161","DOIUrl":"https://doi.org/10.1145/3232161","url":null,"abstract":"The emerging device-to-device communication solutions and the abundance of mobile applications and services make opportunistic networking not only a feasible solution but also an important component of future wireless networks. Specifically, the distribution of locally relevant content could be based on the community of mobile users visiting an area, if long-term content survival can be ensured this way. In this article, we establish the conditions of content survival in such opportunistic networks, considering the user mobility patterns, as well as the time users keep forwarding the content, as the controllable system parameter. We model the content spreading with an epidemic process, and derive a stochastic differential equations based approximation. By means of stability analysis, we determine the necessary user contribution to ensure content survival. We show that the required contribution from the users depends significantly on the size of the population, that users need to redistribute content only in a short period within their stay, and that they can decrease their contribution significantly in crowded areas. Hence, with the appropriate control of the system parameters, opportunistic content sharing can be both reliable and sustainable.","PeriodicalId":105474,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115959643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying Cloud Performance and Dependability","authors":"N. Herbst, A. Bauer, Samuel Kounev, Giorgos Oikonomou, Erwin Van Eyk, George Kousiouris, Athanasia Evangelinou, Rouven Krebs, Tim Brecht, Cristina L. Abad, A. Iosup","doi":"10.1145/3236332","DOIUrl":"https://doi.org/10.1145/3236332","url":null,"abstract":"In only a decade, cloud computing has emerged from a pursuit for a service-driven information and communication technology (ICT), becoming a significant fraction of the ICT market. Responding to the growth of the market, many alternative cloud services and their underlying systems are currently vying for the attention of cloud users and providers. To make informed choices between competing cloud service providers, permit the cost-benefit analysis of cloud-based systems, and enable system DevOps to evaluate and tune the performance of these complex ecosystems, appropriate performance metrics, benchmarks, tools, and methodologies are necessary. This requires re-examining old system properties and considering new system properties, possibly leading to the re-design of classic benchmarking metrics such as expressing performance as throughput and latency (response time). In this work, we address these requirements by focusing on four system properties: (i) elasticity of the cloud service, to accommodate large variations in the amount of service requested, (ii) performance isolation between the tenants of shared cloud systems and resulting performance variability, (iii) availability of cloud services and systems, and (iv) the operational risk of running a production system in a cloud environment. Focusing on key metrics for each of these properties, we review the state-of-the-art, then select or propose new metrics together with measurement approaches. We see the presented metrics as a foundation toward upcoming, future industry-standard cloud benchmarks.","PeriodicalId":105474,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134229250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scale-Out vs Scale-Up","authors":"R. Azimi, Tyler Fox, Wendy Gonzalez, S. Reda","doi":"10.1145/3232162","DOIUrl":"https://doi.org/10.1145/3232162","url":null,"abstract":"ARM 64-bit processing has generated enthusiasm to develop ARM-based servers that are targeted for both data centers and supercomputers. In addition to the server-class components and hardware advancements, the ARM software environment has grown substantially over the past decade. Major development ecosystems and libraries have been ported and optimized to run on ARM, making ARM suitable for server-class workloads. There are two trends in available ARM SoCs: mobile-class ARM SoCs that rely on the heterogeneous integration of a mix of CPU cores, GPGPU streaming multiprocessors (SMs), and other accelerators, and the server-class SoCs that instead rely on integrating a larger number of CPU cores with no GPGPU support and a number of IO accelerators. For scaling the number of processing cores, there are two different paradigms: mobile-class SoCs that use scale-out architecture in the form of a cluster of simpler systems connected over a network, and server-class ARM SoCs that use the scale-up solution and leverage symmetric multiprocessing to pack a large number of cores on the chip. In this article, we present ScaleSoC cluster, which is a scale-out solution based on mobile class ARM SoCs. ScaleSoC leverages fast network connectivity and GPGPU acceleration to improve performance and energy efficiency compared to previous ARM scale-out clusters. We consider a wide range of modern server-class parallel workloads to study both scaling paradigms, including latency-sensitive transactional workloads, MPI-based CPU and GPGPU-accelerated scientific applications, and emerging artificial intelligence workloads. We study the performance and energy efficiency of ScaleSoC compared to server-class ARM SoCs and discrete GPGPUs in depth. We quantify the network overhead on the performance of ScaleSoC and show that packing a large number of ARM cores on a single chip does not necessarily guarantee better performance, due to the fact that shared resources, such as last-level cache, become performance bottlenecks. We characterize the GPGPU accelerated workloads and demonstrate that for applications that can leverage the better CPU-GPGPU balance of the ScaleSoC cluster, performance and energy efficiency improve compared to discrete GPGPUs.","PeriodicalId":105474,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)","volume":"53 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116838840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QMLE","authors":"Weikun Wang, G. Casale, A. Kattepur, M. Nambiar","doi":"10.1145/3233180","DOIUrl":"https://doi.org/10.1145/3233180","url":null,"abstract":"Estimating the demands placed by services on physical resources is an essential step for the definition of performance models. For example, scalability analysis relies on these parameters to predict queueing delays under increasing loads. In this article, we investigate maximum likelihood (ML) estimators for demands at load-independent and load-dependent resources in systems with parallelism constraints. We define a likelihood function based on state measurements and derive necessary conditions for its maximization. We then obtain novel estimators that accurately and inexpensively obtain service demands using only aggregate state data. With our approach, and also thanks to approximation methods for computing marginal and joint distributions for the load-dependent case, confidence intervals can be rigorously derived, explicitly taking into account both topology and concurrency levels of the services. Our estimators and their confidence intervals are validated against simulations and real system measurements for two multi-tier applications, showing high accuracy also in models with load-dependent resources.","PeriodicalId":105474,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121939628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalization of LRU Cache Replacement Policy with Applications to Video Streaming","authors":"E. Friedlander, V. Aggarwal","doi":"10.1145/3345022","DOIUrl":"https://doi.org/10.1145/3345022","url":null,"abstract":"Caching plays a crucial role in networking systems to reduce the load on the network and is commonly employed by content delivery networks (CDNs) to improve performance. One of the commonly used mechanisms, Least Recently Used (LRU), works well for identical file sizes. However, for asymmetric file sizes, the performance deteriorates. This article proposes an adaptation to the LRU strategy, called gLRU, where the file is sub-divided into equal-sized chunks. In this strategy, a chunk of the newly requested file is added in the cache, and a chunk of the least-recently-used file is removed from the cache. Even though approximate analysis for the hit rate has been studied for LRU, the analysis does not extend to gLRU, since the metric of interest is no longer the hit rate as the cache has partial files. This article provides a novel approximation analysis for this policy where the cache may have partial file contents. The approximation approach is validated by simulations. Further, gLRU outperforms the LRU strategy for a Zipf file popularity distribution and censored Pareto file size distribution for the file download times. Video streaming applications can further use the partial cache contents to help the stall duration significantly, and the numerical results indicate significant improvements (32%) in stall duration using the gLRU strategy as compared to the LRU strategy. Furthermore, the gLRU replacement policy compares favorably to two other cache replacement policies when simulated on MSR Cambridge Traces obtained from the SNIA IOTTA repository.","PeriodicalId":105474,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132217978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CloudHeat","authors":"Shutong Chen, Zhi Zhou, Fangming Liu, Zongpeng Li, Shaolei Ren","doi":"10.1145/3199675","DOIUrl":"https://doi.org/10.1145/3199675","url":null,"abstract":"Datacenters are major energy consumers and dissipate an enormous amount of waste heat. Simple outdoor discharging of datacenter heat is energy-consuming and environmentally unfriendly. By harvesting datacenter waste heat and selling to the district heating system (DHS), both energy cost compensation and environment protection can be achieved. To realize such benefits in practice, an efficient market mechanism is required to incentivize the participation of datacenters. This work proposes CloudHeat, an online reverse auction mechanism for the DHS to solicit heat bids from datacenters. To minimize long-term social operational cost of the DHS and the datacenters, we apply a RFHC approach for decomposing the long-term problem into a series of one-round auctions, guaranteeing a small loss in competitive ratio. The one-round optimization is still NP-hard, and we employ a randomized auction framework to simultaneously guarantee truthfulness, polynomial running time, and an approximation ratio of 2. The performance of CloudHeat is validated through theoretical analysis and trace-driven simulation studies.","PeriodicalId":105474,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)","volume":"374 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122838482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}