Server clustering is a common design principle employed by many organisations who require high availability, scalability and easier management of their infrastructure. Servers are typically clustered according to the service they provide whether it be the application(s) installed, the role of the server or server accessibility for example. In order to optimize performance, manage load and maintain availability, servers may migrate from one cluster group to another making it difficult for server monitoring tools to continuously monitor these dynamically changing groups. Server monitoring tools are usually statically configured and with any change of group membership requires manual reconfiguration, an unreasonable task to undertake on large-scale cloud infrastructures. In this paper we present the Cloudlet Control and Management System (C2MS), a system for monitoring and controlling dynamic groups of physical or virtual servers within cloud infrastructures. The C2MS extends Ganglia - an open source scalable system performance monitoring tool - by allowing system administrators to define, monitor and modify server groups without the need for server reconfiguration. In turn administrators can easily monitor group and individual server metrics on large-scale dynamic cloud infrastructures where roles of servers may change frequently. Furthermore, we complement group monitoring with a control element allowing administrator-specified actions to be performed over servers within service groups as well as introduce further customized monitoring metrics. This paper outlines the design, implementation and evaluation of the C2MS.
Birman outline their distributed self-configuring monitoring and adaptation tool called Astrolabe @cite_10 . Astrolabe works like any other monitoring tool by observing the state of an infrastructure where the tool is installed. However it differs by essentially creating a virtual system-wide hierarchical relational database based on a peer-to-peer protocol meaning no central server needs to exist to collect monitoring data. By performing distributed data analysis, Astrolabe can create performance summaries of --- machines typically grouped based on the shortest latency between one another or simply an administrator-specified group --- by data aggregation; a method we use to create graphs of cloudlets.
@cite_10
1997369208 2132237855 2524623326 2162485312
Scalable management and self-organizational capabilities areemerging as central requirements for a generation of large-scale,highly dynamic, distributed applications. We have developed anentirely new distributed information management system calledAstrolabe. Astrolabe collects large-scale system state, permittingrapid updates and providing on-the-fly attribute aggregation. Thislatter capability permits an application to locate a resource, andalso offers a scalable way to track system state as it evolves overtime. The combination of features makes it possible to solve a widevariety of management and self-configuration problems. This paperdescribes the design of the system with a focus upon itsscalability. After describing the Astrolabe service, we presentexamples of the use of Astrolabe for locating resources,publish-subscribe, and distributed synchronization in largesystems. Astrolabe is implemented using a peer-to-peer protocol,and uses a restricted form of mobile code based on the SQL querylanguage for aggregation. This protocol gives rise to a novelconsistency model. Astrolabe addresses several securityconsiderations using a built-in PKI. The scalability of the systemis evaluated using both simulation and experiments; these confirmthat Astrolabe could scale to thousands and perhaps millions ofnodes, with information propagation delays in the tens of seconds. The dramatic growth of computer networks creates both an opportunity and a daunting distributed computing problem for users seeking to build applications that can configure themselves and adapt as disruptions occur. The problem is that data often resides on large numbers of devices and evolves rapidly. Systems that collect data at a single location scale poorly and suffer from single-point failures. Here, we discuss the use of a new system, Astrolabe, to automate self-configuration, monitoring, and to control adaptation. Astrolabe operates by creating a virtual system-wide hierarchical database, which evolves as the underlying information changes. Astrolabe is secure, robust under a wide range of failure and attack scenarios, and imposes low loads even under stress. Traditionally distributed graph processing systems have largely focused on scalability through the optimizations of inter-node communication and load balance. However, they often deliver unsatisfactory overall processing efficiency compared with shared-memory graph computing frameworks. We analyze the behavior of several graph-parallel systems and find that the added overhead for achieving scalability becomes a major limiting factor for efficiency, especially with modern multi-core processors and high-speed interconnection networks. Based on our observations, we present Gemini, a distributed graph processing system that applies multiple optimizations targeting computation performance to build scalability on top of efficiency. Gemini adopts (1) a sparse-dense signal-slot abstraction to extend the hybrid push-pull computation model from shared-memory to distributed scenarios, (2) a chunk-based partitioning scheme enabling low-overhead scaling out designs and locality-preserving vertex accesses, (3) a dual representation scheme to compress accesses to vertex indices, (4) NUMA-aware sub-partitioning for efficient intra-node memory accesses, plus (5) locality-aware chunking and fine-grained work-stealing for improving both internode and intra-node load balance, respectively. Our evaluation on an 8-node high-performance cluster (using five widely used graph applications and five real-world graphs) shows that Gemini significantly outperforms all well-known existing distributed graph processing systems, delivering up to 39.8× (from 8.91×) improvement over the fastest among them. Traditional cluster monitoring approaches consider nodes in singleton, using manufacturer-specified extreme limits as thresholds for failure "prediction". We have developed a tool, OVIS, for monitoring and analysis of large computational platforms which, instead, uses a statistical approach to characterize single device behaviors from those of a large number of statistically similar devices. Baseline capabilities of OVIS include the visual display of deterministic information about state variables (e.g., temperature, CPU utilization, fan speed) and their aggregate statistics. Visual consideration of the cluster as a comparative ensemble, rather than as singleton nodes, is an easy and useful method for tuning cluster configuration and determining effects of realtime changes. Additionally, OVIS incorporates a novel Bayesian inference scheme to dynamically infer models for the normal behavior of a system and to determine bounds on the probability of values evinced in the system. Individual node values that are unlikely given the current applicable model are flagged as aberrant. This can be a much earlier indicator of problems than waiting for the crossing of some threshold that is necessarily set high to preclude too many false alarms. We present OVIS and discuss its applications in cluster configuration and environmental tuning and to abnormality and problem discovery in our production clusters.