Workload Consolidation Research Articles

Holistic Routing Algorithm Design to Support Workload Consolidation in NoCs

To provide efficient, high-performance routing algorithms, a holistic approach should be taken. The key aspects of routing algorithm design include adaptivity, path selection strategy, VC allocation, isolation, and hardware implementation cost; these design aspects are not independent. The key contribution of this work lies in the design of a novel selection strategy, Destination-Based Selection Strategy (DBSS), which targets interference that can arise in many-core systems running consolidation workloads. In the process of this design, we holistically consider all aspects to ensure an efficient design. Existing routing algorithms largely overlook issues associated with workload consolidation. Locally adaptive algorithms do not consider enough status information to avoid network congestion. Globally adaptive routing algorithms attack this issue by utilizing network status beyond neighboring nodes. However, they may suffer from interference, coupling the behavior of otherwise independent applications. To address these issues, DBSS leverages both local and nonlocal network status to provide more effective adaptivity. More importantly, by integrating the destination into the selection procedure, DBSS mitigates interference and offers dynamic isolation among applications. Results show that DBSS offers better performance than the best baseline selection strategy and improves the energy-delay product for medium and high injection rates; it is well suited for workload consolidation.

COMMA

Multi-tier applications are widely deployed in today's virtualized cloud computing environments. At the same time, management operations in these virtualized environments, such as load balancing, hardware maintenance, workload consolidation, etc., often make use of live virtual machine (VM) migration to control the placement of VMs. Although existing solutions are able to migrate a single VM efficiently, little attention has been devoted to migrating related VMs in multi-tier applications. Ignoring the relatedness of VMs during migration can lead to serious application performance degradation. This paper formulates the multi-tier application migration problem, and presents a new communication-impact-driven coordinated approach, as well as a system called COMMA that realizes this approach. Through extensive testbed experiments, numerical analyses, and a demonstration of COMMA on Amazon EC2, we show that this approach is highly effective in minimizing migration's impact on multi-tier applications' performance.

Efficient memory virtualization for Cross-ISA system mode emulation

Cross-ISA system-mode emulation has many important applications. For example, Cross-ISA system-mode emulation helps computer architects and OS developers trace and debug kernel execution-flow efficiently by emulating a slower platform (such as ARM) on a more powerful plat-form (such as an x86 machine). Cross-ISA system-mode emulation also enables workload consolidation in data centers with platforms of different instruction-set architectures (ISAs). However, system-mode emulation is much slower. One major overhead in system-mode emulation is the multi-level memory address translation that maps guest virtual address to host physical address. Shadow page tables (SPT) have been used to reduce such overheads, but primarily for same-ISA virtualization. In this paper we propose a novel approach called embedded shadow page tables (ESPT). EPST embeds a shadow page table into the address space of a cross-ISA dynamic binary translation (DBT) and uses hardware memory management unit in the CPU to translate memory addresses, instead of software translation in a current DBT emulator like QEMU. We also use the larger address space on modern 64-bit CPUs to accommodate our DBT emulator so that it will not interfere with the guest operating system. We incorporate our new scheme into QEMU, a popular, retargetable cross-ISA system emulator. SPEC CINT2006 benchmark results indicate that our technique achieves an average speedup of 1.51 times in system mode when emulating ARM on x86, and a 1.59 times speedup for emulating IA32 on x86_64.

Server Power Modeling for Run-time Energy Optimization of Cloud Computing Facilities

As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. The average consumption of a single data center is equivalent to the energy consumption of 25.000 households. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. This work proposes an automatic method, based on Multi-Objective Particle Swarm Optimization, for the identification of power models of enterprise servers in Cloud data centers. Our approach, as opposed to previous procedures, does not only consider the workload consolidation for deriving the power model, but also incorporates other non traditional factors like the static power consumption and its dependence with temperature. Our experimental results shows that we reach slightly better models than classical approaches, but simul- taneously simplifying the power model structure and thus the numbers of sensors needed, which is very promising for a short-term energy prediction. This work, validated with real Cloud applications, broadens the possibilities to derive efficient energy saving techniques for Cloud facilities.

Algorithms for Task Consolidation Problem in a Cloud Computing Environment

Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Task consolidation problem in cloud computing systems became an important approach to streamline resource usage which improves energy efficiency. The task consolidation is also known as workload consolidation problem which is the process of assigning set of tasks to set of resources without violating time constraints. Three existing energy conscious heuristics such as ECTC (Energy-Conscious Task Consolidation) Task Consolidation Algorithm and MaxUtil (Maximum rate Utilization) Task Consolidation Algorithm and Bi-objective Task Consolidation algorithm offering different energy saving possibilities were analyzed in this study. The cost functions incorporated effectively capture energy saving possibilities and their capability has been verified by evaluation study. The Bi-objective Task Consolidation algorithm combines the two heuristics to construct the corresponding bi-objective search space. The efficiency of proposed algorithm was proved thought evaluation study consisting of different simulations carried out.

Heterogeneous Workload Consolidation for Efficient Management of Data Centers in Cloud Computing

Cloud computing is a recent innovation, which provides various services on a usage based payment model. The rapid expansion in data centers has triggered the dramatic increase in energy used, operational cost and its effect on the environment in terms of carbon footprints. To reduce power consumption, it is necessary to consolidate the hosting workloads. In this paper, we present a Single Threshold technique for efficient consolidation of heterogeneous workloads. Our technique focuses on the energy consumption of the data center due to the heterogeneity of the workloads and also gives information about the SLA violations. The experimental results demonstrate that our technique is efficient for the data centers to consolidate the heterogeneous workloads. General Terms Cloud Computing, Workload Consolidation Algorithms.

Copy rate synchronization with performance guarantees for work consolidation in storage clusters

As storage in data centers is increasing rapidly, it has become critical to find ways to operate efficiently this important component of a data center. Often, it has been proposed to consolidate the storage workload into a subset of storage devices and shutdown the unused ones with the purpose of preserving power. In many cases storage workload consolidation requires some amount of data to be copied from one device or set to the next. While storage workload consolidation techniques focus on extending power savings with minimal penalty in the performance of a data center, less attention is paid to the process of seamlessly integrating the data copy phase into the overall storage workload consolidation technique. Specifically, in this paper, we propose an analytic framework that synchronizes the pace of copying data between two storage devices (or nodes) such that performance is maintained within predefined targets. As such, we avoid either undesired performance degradation caused by aggressively scheduling the data copy task or a slow copy process caused by conservative scheduling. We show with extensive experimentation that the framework is robust and that it provides an important step toward automating storage consolidation and high power savings.

IO performance prediction in consolidated virtualized environments (abstracts only)

We propose a trace-driven approach to predict the performance degradation of disk request response times due to storage device contention in consolidated virtualized environments. Our performance model evaluates a queueing network with fair share scheduling using trace-driven simulation. The model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same virtualized server. The model parameter estimation relies on a search technique that tries to estimate the splitting and merging of blocks at the the Virtual Machine Monitor (VMM) level in the case of multiple competing VMs. Simulation experiments based on traces of the Postmark and FFSB disk benchmarks show that our model is able to accurately predict the impact of workload consolidation on VM disk IO response times.

Abstract only

We propose a trace-driven approach to predict the performance degradation of disk request response times due to storage device contention in consolidated virtualized environments. Our performance model evaluates a queueing network with fair share scheduling using trace-driven simulation. The model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same virtualized server. The model parameter estimation relies on a search technique that tries to estimate the splitting and merging of blocks at the the Virtual Machine Monitor (VMM) level in the case of multiple competing VMs. Simulation experiments based on traces of the Postmark and FFSB disk benchmarks show that our model is able to accurately predict the impact of workload consolidation on VM disk IO response times.

DBAR

With the emergence of many-core architectures, it is quite likely that multiple applications will run concurrently on a system. Existing locally and globally adaptive routing algorithms largely overlook issues associated with workload consolidation. The shortsightedness of locally adaptive routing algorithms limits performance due to poor network congestion avoidance. Globally adaptive routing algorithms attack this issue by introducing a congestion propagation network to obtain network status information beyond neighboring nodes. However, they may suffer from intra- and inter-application interference during output port selection for consolidated workloads, coupling the behavior of otherwise independent applications and negatively affecting performance. To address these two issues, we propose Destination-Based Adaptive Routing (DBAR). We design a novel low-cost congestion propagation network that leverages both local and non-local network information for more accurate congestion estimates. Thus, DBAR offers effective adaptivity for congestion beyond neighboring nodes. More importantly, by integrating the destination into the selection function, DBAR mitigates intra- and inter-application interference and offers dynamic isolation among regions. Experimental results show that DBAR can offer better performance than the best baseline algorithm for all measured configurations; it is well suited for workload consolidation. The wiring overhead of DBAR is low and DBAR provides improvement in the energy-delay product for medium and high injection rates.

Memory systems in the many-core era

The memory subsystem is a fundamental performance and energy bottleneck in almost all computing systems. Recent trends towards increasingly more cores on die, consolidation of diverse workloads on a single chip, and difficulty of DRAM scaling impose new requirements and exacerbate old demands on the memory system. In particular, the need for memory bandwidth and capacity is increasing [14], applications' interference in memory system increasingly limits system performance and makes the system hard to control [12], memory energy and power are key design concerns [8], and DRAM technology consumes significant amount of energy and does not scale down easily to smaller technology nodes [7]. Fortunately, some promising solution directions exist. In this talk, we will examine recent technology, application, and architecture trends motivating a fundamental rethinking of the memory hierarchy. Based on this motivation, we will describe requirements from an ideal memory system suitable for the many-core era. The talk will examine questions one would need to answer in approximating the ideal memory system and possible avenues that seem promising for the research community to explore. In particular, we will focus on the problem of uncontrolled inter-application interference in the memory system and draw upon our experiences in solving it by designing quality-of-service (QoS) aware memory controllers [5, 6, 9, 10, 11, 12], interconnects [1 2 13], and entire memory systems. We will make a case forapplication- and QoS-aware design of memory systems and [3, 4]integrated/cooperative design of cores, interconnects, and memory components to optimize the overall system.

Workload Portfolio Optimization for Virtualized Computer Systems Based on Semiparametric Quantile Function Estimation

SummaryThe latest technologies of server virtualization allow multiple computer workloads to share the same physical system dynamically while protecting them from interference from each other. Consolidation of workloads from stand-alone systems into virtualized systems requires accurate capacity sizing and optimal portfolio design to maximize the benefit of virtualization. This requirement often translates into a demand for accurate estimation of high quantiles from a limited amount of data for hundreds of workloads with diverse statistical characteristics. To deal with the problem, a semiparametric method of quantile function estimation is considered. The method employs the generalized Pareto distribution to model the high quantiles that exceed a certain threshold and retains the sample quantiles below the threshold. An automatic procedure is proposed for adaptive threshold and estimator selection and for adaptive data trimming. A simulation study shows that the procedure proposed is superior to the non-parametric sample quantile method for a variety of distributions. The procedure is applied to a portfolio optimization problem for computer workload consolidation with real data.

Techniques to implement in green data centres to achieve energy efficiency and reduce global warming effects

Energy demand in data centre industry is growing rapidly as computing technology changes and Information Technology (IT) professionals seek to maximise performance of data centres. A multitude of methods have been used to estimate and quantify energy intensity. Rising energy costs, continuing concerns about global economic downturn and global warming effects has leaded ways for energy efficient data centres. The goal of computer system design has been shifted to power and energy efficiency. This paper highlights strategies and techniques that provide energy savings in data centre like reducing frequent hardware purchases, power/cooling cost reductions, green metrics, shrinking data storage, workload consolidation and reducing physical servers.

XHive: Efficient Cooperative Caching for Virtual Machines

Since a virtual machine independently uses its own caching policy, redundant disk operations exacerbate the I/O virtualization overhead when virtual machines access large amounts of data on shared storage. This paper presents XHive, an efficient cooperative caching system that is implemented at the virtualization layer, for consolidated environments. Our proposed scheme globally manages buffer caches of consolidated virtual machines in order to accommodate a shared working set in machine memory. A singlet, which is a block cached solely by a virtual machine, is preferentially given more chances to be cached in machine memory by XHive, when it is evicted by a guest operating system. For efficient use of limited memory, singlets are cached in memory that is collaboratively donated from idle memory of virtual machines. Our evaluation shows that XHive significantly reduces disk I/O operations for shared working sets, thereby achieving high read performance and scalability. Improved scalability enables a high degree of workload consolidation with respect to virtual machines that have shared working sets.

Power Consumption Prediction and Power-Aware Packing in Consolidated Environments

Consolidation of workloads has emerged as a key mechanism to dampen the rapidly growing energy expenditure within enterprise-scale data centers. To gainfully utilize consolidation-based techniques, we must be able to characterize the power consumption of groups of colocated applications. Such characterization is crucial for effective prediction and enforcement of appropriate limits on power consumption-power budgets-within the data center. We identify two kinds of power budgets: 1) an average budget to capture an upper bound on long-term energy consumption within that level and 2) a sustained budget to capture any restrictions on sustained draw of current above a certain threshold. Using a simple measurement infrastructure, we derive power profiles-statistical descriptions of the power consumption of applications. Based on insights gained from detailed profiling of several applications-both individual and consolidated-we develop models for predicting average and sustained power consumption of consolidated applications. We conduct an experimental evaluation of our techniques on a Xen-based server that consolidates applications drawn from a diverse pool. For a variety of consolidation scenarios, we are able to predict average power consumption within five percent error margin and sustained power within 10 percent error margin. Using prediction techniques allows us to ensure safe yet efficient system operation-in a representative case, we are able to improve the number of applications consolidated on a server from two to three (compared to existing baseline techniques) by choosing the appropriate power state that satisfies the power budgets associated with the server.

Resource pool management: Reactive versus proactive or let’s be friends

The consolidation of multiple workloads and servers enables the efficient use of server and power resources in shared resource pools. We employ a trace-based workload placement controller that uses historical information to periodically and proactively reassign workloads to servers subject to their quality of service objectives. A reactive migration controller is introduced that detects server overload and underload conditions. It initiates the migration of workloads when the demand for resources exceeds supply. Furthermore, it dynamically adds and removes servers to maintain a balance of supply and demand for capacity while minimizing power usage. A host load simulation environment is used to evaluate several different management policies for the controllers in a time effective manner. A case study involving three months of data for 138 SAP applications compares three integrated controller approaches with the use of each controller separately. The study considers trade-offs between: (i) required capacity and power usage, (ii) resource access quality of service for CPU and memory resources, and (iii) the number of migrations. Our study sheds light on the question of whether a reactive controller or proactive workload placement controller alone is adequate for resource pool management. The results show that the most tightly integrated controller approach offers the best results in terms of capacity and quality but requires more migrations per hour than the other strategies.

Scoring and thresholding for availability

As the capacity of hardware systems has grown and workload consolidation has taken place, the volume of performance metrics and diagnostic data streams has outscaled the capability of people to handle these systems using traditional methods. As work of different types (such as database, batch, and Web processing), each in its own monitoring silo, runs concurrently on a single image (operating system instance), both the complexity and the business consequences of a single image failure have increased. This paper presents two techniques for generating actionable information out of the overwhelming amount of performance and diagnostic data available to human analysts. Failure scoring is used to identify high-risk failure events that may be obscured in the myriad system events. This replaces human expertise in scanning tens of thousands of records per day and results in a short, prioritized list for action by systems staff. Adaptive thresholding is used to drive predictive and descriptive machine-learning-based modeling to isolate and identify misbehaving processes and transactions. The attraction of this technique is that it does not require human intervention and can be reapplied continually, resulting in models that are not brittle. Both techniques reduce the quantity and increase the relevance of data available for programmatic and human processes.