Hani Jamjoom

Application-aware virtual machine migration in data centers

While virtual machine (VM) migration is allowing data centers to rebalance workloads across physical machines, the promise of a maximally utilized infrastructure is yet to be realized. Part of the challenge is due to the inherent dependencies between VMs comprising a multi-tier application, which introduce complex load interactions between the underlying physical servers. For example, simply moving an overloaded VM to a (random) underloaded physical machine can inadvertently overload the network. We introduce AppAware—a novel, computationally efficient scheme for incorporating (1) inter-VM dependencies and (2) the underlying network topology into VM migration decisions. Using simulations, we show that our proposed method decreases network traffic by up to 81%compared to a well known alternative VM migration method that is not application-aware.

Mizan: a system for dynamic load balancing in large-scale graph processing

Pregel [23] was recently introduced as a scalable graph mining system that can provide significant performance improvements over traditional MapReduce implementations. Existing implementations focus primarily on graph partitioning as a preprocessing step to balance computation across compute nodes. In this paper, we examine the runtime characteristics of a Pregel system. We show that graph partitioning alone is insufficient for minimizing end-to-end computation. Especially where data is very large or the runtime behavior of the algorithm is unknown, an adaptive approach is needed. To this end, we introduce Mizan, a Pregel system that achieves efficient load balancing to better adapt to changes in computing needs. Unlike known implementations of Pregel, Mizan does not assume any a priori knowledge of the structure of the graph or behavior of the algorithm. Instead, it monitors the runtime characteristics of the system. Mizan then performs efficient fine-grained vertex migration to balance computation and communication. We have fully implemented Mizan; using extensive evaluation we show that---especially for highly-dynamic workloads---Mizan provides up to 84% improvement over techniques leveraging static graph pre-partitioning.

The Xen-Blanket: virtualize once, run everywhere

Current Infrastructure as a Service (IaaS) clouds operate in isolation from each other. Slight variations in the virtual machine (VM) abstractions or underlying hypervisor services prevent unified access and control across clouds. While standardization efforts aim to address these issues, they will take years to be agreed upon and adopted, if ever. Instead of standardization, which is by definition provider-centric, we advocate a user-centric approach that gives users an unprecedented level of control over the virtualization layer. We introduce the Xen-Blanket, a thin, immediately deployable virtualization layer that can homogenize todays diverse cloud infrastructures. We have deployed the Xen-Blanket across Amazons EC2, an enterprise cloud, and a private setup at Cornell University. We show that a user-centric approach to homogenize clouds can achieve similar performance to a paravirtualized environment while enabling previously impossible tasks like cross-provider live migration. The Xen-Blanket also allows users to exploit resource management opportunities like oversubscription, and ultimately can reduce costs for users.

Overdriver: handling memory overload in an oversubscribed cloud

With the intense competition between cloud providers, oversubscription is increasingly important to maintain profitability. Oversubscribing physical resources is not without consequences: it increases the likelihood of overload. Memory overload is particularly damaging. Contrary to traditional views, we analyze current data center logs and realistic Web workloads to show that overload is largely transient: up to 88.1% of overloads last for less than 2 minutes. Regarding overload as a continuum that includes both transient and sustained overloads of various durations points us to consider mitigation approaches also as a continuum, complete with tradeoffs with respect to application performance and data center overhead. In particular, heavyweight techniques, like VM migration, are better suited to sustained overloads, whereas lightweight approaches, like network memory, are better suited to transient overloads. We present Overdriver, a system that adaptively takes advantage of these tradeoffs, mitigating all overloads within 8% of well-provisioned performance. Furthermore, under reasonable oversubscription ratios, where transient overload constitutes the vast majority of overloads, Overdriver requires 15% of the excess space and generates a factor of four less network traffic than a migration-only approach.

Virtual machine migration in an over-committed cloud

While early emphasis of Infrastructure as a Service (IaaS) clouds was on providing resource elasticity to end users, providers are increasingly interested in over-committing their resources to maximize the utilization and returns of their capital investments. In principle, over-committing resources hedges that users - on average - only need a small portion of their leased resources. When such hedge fails (i.e., resource demand far exceeds available physical capacity), providers must mitigate this provider-induced overload, typically by migrating virtual machines (VMs) to underutilized physical machines. Recent works on VM placement and migration assume the availability of target physical machines [1], [2]. However, in an over-committed cloud data center, this is not the case. VM migration can even trigger cascading overloads if performed haphazardly. In this paper, we design a new VM migration algorithm (called Scattered) that minimizes VM migrations in over-committed data centers. Compared to a traditional implementation, our algorithm can balance host utilization across all time epochs. Using real-world data traces from an enterprise cloud, we show that our migration algorithm reduces the risk of overload, minimizes the number of needed migrations, and has minimal impact on communication cost between VMs.

Pico replication: a high availability framework for middleboxes

Middleboxes are being rearchitected to be service oriented, composable, extensible, and elastic. Yet system-level support for high availability (HA) continues to introduce significant performance overhead. In this paper, we propose Pico Replication (PR), a system-level framework for middleboxes that exploits their flow-centric structure to achieve low overhead, fully customizable HA. Unlike generic (virtual machine level) techniques, PR operates at the flow level. Individual flows can be checkpointed at very high frequencies while the middlebox continues to process other flows. Furthermore, each flow can have its own checkpoint frequency, output buffer and target for backup, enabling rich and diverse policies that balance---per-flow---performance and utilization. PR leverages OpenFlow to provide near instant flow-level failure recovery, by dynamically rerouting a flows packets to its replication target. We have implemented PR and a flow-based HA policy. In controlled experiments, PR sustains checkpoint frequencies of 1000Hz, an order of magnitude improvement over current VM replication solutions. As a result, PR drastically reduces the overhead on end-to-end latency from 280% to 15.5% and throughput overhead from 99.5% to 3.2%.

Persistent dropping: an efficient control of traffic aggregates

Flash crowd events (FCEs) present a real threat to the stability of routers and end-servers. Such events are characterized by a large and sustained spike in client arrival rates, usually to the point of service failure. Traditional rate-based drop policies, such as Random Early Drop (RED), become ineffective in such situations since clients tend to be persistent, in the sense that they make multiple retransmission attempts before aborting their connection. As it is built into TCPs congestion control, this persistence is very widespread, making it a major stumbling block to providing responsive aggregate traffic controls. This paper focuses on analyzing and building a coherent model of the effects of client persistence on the controllability of aggregate traffic. Based on this model, we propose a new drop strategy called persistent dropping to regulate the arrival of SYN packets and achieves three important goals: (1) it allows routers and end-servers to quickly converge to their control targets without sacrificing fairness, (2) it minimizes the portion of client delay that is attributed to the applied controls, and (3) it is both easily implementable and computationally tractable. Using a real implementation of this controller in the Linux kernel, we demonstrate its efficacy, up to 60% delay reduction for drop probabilities less than 0.5.

Stateless Network Functions

Newly virtualized network functions (like firewalls, routers, and intrusion detection systems) should be easy to consume. Despite recent efforts to improve their elasticity and high availability, network functions continue to maintain important flow state, requiring traditional development and deployment life cycles. At the same time, many cloud-scale applications are being rearchitected to be stateless by cleanly pushing application state into dedicated caches or backend stores. This state separation is enabling these applications to be more agile and support the so-called continuous deployment model. In this paper, we propose that network functions should be similarly redesigned to be stateless. Drawing insights from different classes of network functions, we describe how stateless network functions can leverage recent advances in low-latency network systems to achieve acceptable performance. Our Click-based prototype integrates with RAMCloud; using NAT as an example network function, we demonstrate that we are able to create stateless network functions that maintain the desired performance.

Rule-Based Problem Classification in IT Service Management

Problem management is a critical and expensive element for delivering IT service management and touches various levels of managed IT infrastructure. While problem management has been mostly reactive, recent work is studying how to leverage large problem ticket information from similar IT infrastructures to probatively predict the onset of problems. Because of the sheer size and complexity of problem tickets, supervised learning algorithms have been the method of choice for problem ticket classification, relying on labeled (or pre-classified) tickets from one managed infrastructure to automatically create signatures for similar infrastructures. However, where there are insufficient preclassified data, leveraging human expertise to develop classification rules can be more efficient. In this paper, we describe a rule-based crowdsourcing approach, where experts can author classification rules and a social networkingbased platform (called xPad) is used to socialize and execute these rules by large practitioner communities. Using real data sets from several large IT delivery centers, we demonstrate that this approach balances between two key criteria: accuracy and cost effectiveness.

SCOOP: Automated Social Recommendation in Enterprise Process Management

The interplay between labor arbitrage and consistent service delivery in IT outsourcing continues to drive business process standardization. New emerging standards, like ITIL, is defining an industry-wide taxonomy for IT service management. These standards are often at a high level and require substantial investment from service providers to define and implement these standards across the various low-level processes they offer. This paper presents a process management system, called Cyano, that uses social networks and recommendation to greatly increase the effectiveness of process capture and knowledge maintenance. We particularly focus on Cyanos social recommendation engine, called SCOOP, which utilizes the intrinsic graph property of process content for recommendation. More specifically, SCOOP maintains a user-process interaction graph Gand computes the user-to-user similarity scores using the random walk with restart on G. Finally, we evaluate SCOOP in the context of a large-scale deployment of Cyano, with thousands of processes and users.