Optimal Power Cost Management Using Stored Energy in Data Centers

Since the electricity bill of a data center constitutes a significant portion of its overall operational costs, reducing this has become important. We investigate cost reduction opportunities that arise by the use of uninterrupted power supply (UPS) units as energy storage devices. This represents a deviation from the usual use of these devices as mere transitional fail-over mechanisms between utility and captive sources such as diesel generators. We consider the problem of opportunistically using these devices to reduce the time average electric utility bill in a data center. Using the technique of Lyapunov optimization, we develop an online control algorithm that can optimally exploit these devices to minimize the time average cost. This algorithm operates without any knowledge of the statistics of the workload or electricity cost processes, making it attractive in the presence of workload and pricing uncertainties. An interesting feature of our algorithm is that its deviation from optimality reduces as the storage capacity is increased. Our work opens up a new area in data center power management.Comment: Full version of Sigmetrics 2011 pape

To Move or Not To Move: The Economics of Cloud Computing

Abstract Cloud-based hosting promises cost advantages over conventional in-house (on-premise) application deployment. One important question when considering a move to the cloud is whether it makes sense for 'my' application to migrate to the cloud. This question is challenging to answer due to following reasons. Although many potential benefits of migrating to the cloud can be enumerated, some benefits may not apply to my application. Also, there can be multiple ways in which an application might make use of the facilities offered by cloud providers. Answering these questions requires an in-depth understanding of the cost implications of all the possible choices specific to 'my' circumstances. In this study We identify an initial set of key factors affecting the costs of a deployement choice. Using benchmarks representing two different applications (TPC-W and TPC-E) we investigate the evolution of costs for different deployment choices. We show that application characteristics such as workload intensity, growth rate, storage capacity and software licensing costs produce complex combined effect on overall costs. We also discuss issues regarding workload variance and horizontal partitioning

Maintaining Mutual Consistency for Cached Web Objects

Existing web proxy caches employ cache consistency mechanisms to ensure that locally cached data is consistent with that at the server. In this paper, we argue that techniques for maintaining consistency of individual objects are not sufficient—a proxy should employ additional mechanisms to ensure that related web objects are mutually consistent with one another. We formally define the notion of mutual consistency and the semantics provided by a mutual consistency mechanism to end-users. We then present techniques for maintaining mutual consistency in the temporal and value domains. A novel aspect of our techniques is that they can adapt to the variations in the rate of change of the source data, resulting in judicious use of proxy and network resources. We evaluate our approaches using real-world web traces and show that (i) careful tuning can result in substantial savings in the network overhead incurred without any substantial loss in fidelity of the consistency guarantees, and (ii) the incremental cost of providing mutual consistency guarantees over mechanisms to provide individual consistency guarantees is small

LEGOStore: A Linearizable Geo-Distributed Store Combining Replication and Erasure Coding

We design and implement LEGOStore, an erasure coding (EC) based linearizable data store over geo-distributed public cloud data centers (DCs). For such a data store, the confluence of the following factors opens up opportunities for EC to be latency-competitive with replication: (a) the necessity of communicating with remote DCs to tolerate entire DC failures and implement linearizability; and (b) the emergence of DCs near most large population centers. LEGOStore employs an optimization framework that, for a given object, carefully chooses among replication and EC, as well as among various DC placements to minimize overall costs. To handle workload dynamism, LEGOStore employs a novel agile reconfiguration protocol. Our evaluation using a LEGOStore prototype spanning 9 Google Cloud Platform DCs demonstrates the efficacy of our ideas. We observe cost savings ranging from moderate (5-20\%) to significant (60\%) over baselines representing the state of the art while meeting tail latency SLOs. Our reconfiguration protocol is able to transition key placements in 3 to 4 inter-DC RTTs ($<$ 1s in our experiments), allowing for agile adaptation to dynamic conditions

On a caching system with object sharing

We consider a content-caching system thatis shared by a number of proxies. The cache could belocated in an edge-cloud datacenter and the proxies couldeach serve a large population of mobile end-users. Eachproxy operates its own LRU-list of a certain capacity inthe shared cache. The length of objects simultaneouslyappearing in plural LRU-lists is equally divided amongthem,i.e., object sharing among the LRUs. We provide a "working-set" approximation for this system to quicklyestimate the cache-hit probabilities under such objectsharing, which can be used to facilitate admission control.Also, a way to reduce ripple evictions,i.e.,setrequestoverhead, is suggested. We give numerical results for ourMemCacheD with Object Sharing (MCD-OS) prototype

Dynamic resource management in Internet hosting platforms

Internet applications such as on-line news, retail, and financial sites have become commonplace in recent years. Due to the prevalence of these applications, platforms that host them have become an important and attractive business. These platforms, called hosting platforms, typically employ large clusters of servers to host multiple applications. Hosting platforms provide performance guarantees to the hosted applications, such as guarantees on response time or throughput, in return for revenue. Two key features of Internet applications make the design of hosting platforms challenging. First, modern Internet applications are extremely complex. Existing resource management solutions rely on simple abstractions of these applications and are therefore fail to accurately capture this complexity. Second, these applications exhibit highly dynamic workloads with multi-time-scale variations. Managing the resources in a hosting platform to realize the often opposing goals of meeting application performance targets and achieving high resource utilization is therefore a difficult endeavor. In this thesis, we present resource management mechanisms that an Internet hosting platform can employ to address these challenges. Our solution consists of resource management mechanisms operating at multiple time-scales. We develop a predictive dynamic capacity provisioning technique for Internet applications that operates at the time-scale of hours or days. A key ingredient of this technique is a model of an Internet application that is used for deriving the resource requirements of the application. We employ both queuing theory and empirical measurements to devise models of Internet applications. The second mechanism is a reactive provisioning technique that operates at the time-scale of a few minutes and utilizes virtual machine monitors for agile switching of servers in the hosting platform among applications. Finally, we develop a policing technique that operates at a per-request level. This technique allows a hosted application to remain operational even under extreme overloads where the arrival rates are an order of magnitude higher than the provisioned capacity. Our experiments on a prototype hosting platform consisting of forty Linux machines demonstrate the utility and feasibility of our techniques