Load Shedding in Mobile Systems with MobiQual
Abstract
In location-based, mobile continual query (CQ) systems, two key measures of quality-of- service (QoS) are: freshness and accuracy. To achieve freshness, the CQ server must perform frequent query reevaluations. To attain accuracy, the CQ server must receive and process frequent position updates from the mobile nodes. However, it is often difficult to obtain fresh and accurate CQ results simultaneously, due to 1) limited resources in computing and communication and 2) fast-changing load conditions caused by continuous mobile node movement. Hence, a key challenge for a mobile CQ system is: How do we achieve the highest possible quality of the CQ results, in both freshness and accuracy, with currently available resources? In this paper, we formulate this problem as a load shedding one, and develop MobiQual—a QoS-aware approach to performing both update load shedding and query load shedding. The design of MobiQual highlights three important features. 1) Differentiated load shedding: We apply different amounts of query load shedding and update load shedding to different groups of queries and mobile nodes, respectively. 2) Per-query QoS specification: Individualized QoS specifications are used to maximize the overall freshness and accuracy of the query results. 3) Lowcost adaptation: MobiQual dynamically adapts, with a minimal overhead, to changing load conditions and available resources. We conduct a set of comprehensive experiments to evaluate the effectiveness of MobiQual. The results show that, through a careful combination of update and query load shedding, the MobiQual approach leads to much higher freshness and accuracy in the query results in all cases, compared to existing approaches that lack the QoS-awareness properties of MobiQual, as well as the solutions that perform query-only or update-only load shedding.
Abstract
In location-based, mobile continual query (CQ) systems, two key measures of quality-of- service (QoS) are: freshness and accuracy. To achieve freshness, the CQ server must perform frequent query reevaluations. To attain accuracy, the CQ server must receive and process frequent position updates from the mobile nodes. However, it is often difficult to obtain fresh and accurate CQ results simultaneously, due to 1) limited resources in computing and communication and 2) fast-changing load conditions caused by continuous mobile node movement. Hence, a key challenge for a mobile CQ system is: How do we achieve the highest possible quality of the CQ results, in both freshness and accuracy, with currently available resources? In this paper, we formulate this problem as a load shedding one, and develop MobiQual—a QoS-aware approach to performing both update load shedding and query load shedding. The design of MobiQual highlights three important features. 1) Differentiated load shedding: We apply different amounts of query load shedding and update load shedding to different groups of queries and mobile nodes, respectively. 2) Per-query QoS specification: Individualized QoS specifications are used to maximize the overall freshness and accuracy of the query results. 3) Lowcost adaptation: MobiQual dynamically adapts, with a minimal overhead, to changing load conditions and available resources. We conduct a set of comprehensive experiments to evaluate the effectiveness of MobiQual. The results show that, through a careful combination of update and query load shedding, the MobiQual approach leads to much higher freshness and accuracy in the query results in all cases, compared to existing approaches that lack the QoS-awareness properties of MobiQual, as well as the solutions that perform query-only or update-only load shedding.
Existing System:
To the best of our knowledge, none of the existing work has exploited the potential of performing load shedding to maximize the application-level freshness and accuracy of mobile queries. In contrast to existing work on scalable query processing and indexing techniques, MobiQual provides a QoSaware framework for performing both update load shedding and query load shedding, in order to provide highly accurate and fresh query results, even under limited resources or overload conditions. Moreover, as a complementary solution, MobiQual can easily take advantage of existing query processing and indexing techniques.
PROPOSED SYSTEM
The salient feature of MobiQual design is its ability to perform dynamic update load shedding and query load shedding according to changing workload characteristics and resource constraints, and its ability to reduce or avoid severe performance degradation in query result quality under such conditions.
Mobi- Qual employs query grouping and space partitioning techniques to reduce the adaptation time required for re-configuring the system in response to high system dynamics, such as the number of queries, the number of mobile nodes, and the evolving movement patterns.
Hardware Requirements:
To the best of our knowledge, none of the existing work has exploited the potential of performing load shedding to maximize the application-level freshness and accuracy of mobile queries. In contrast to existing work on scalable query processing and indexing techniques, MobiQual provides a QoSaware framework for performing both update load shedding and query load shedding, in order to provide highly accurate and fresh query results, even under limited resources or overload conditions. Moreover, as a complementary solution, MobiQual can easily take advantage of existing query processing and indexing techniques.
PROPOSED SYSTEM
The salient feature of MobiQual design is its ability to perform dynamic update load shedding and query load shedding according to changing workload characteristics and resource constraints, and its ability to reduce or avoid severe performance degradation in query result quality under such conditions.
Mobi- Qual employs query grouping and space partitioning techniques to reduce the adaptation time required for re-configuring the system in response to high system dynamics, such as the number of queries, the number of mobile nodes, and the evolving movement patterns.
Hardware Requirements:
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System
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Hard Disk
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Floppy Drive
-
Monitor
-
Mouse
-
Ram
: Pentium IV 2.4 GHz.
: 40 GB.
: 1.44 Mb.
: 15 VGA Colour.
: Logitech. : 256 Mb.
: 1.44 Mb.
: 15 VGA Colour.
: Logitech. : 256 Mb.
Software Requirements:
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Operating system
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Coding Language
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Tool Used
Modules:
: - Windows XP Professional.
: - Java.
: - Eclipse.
: - Eclipse.
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Client Model
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Server Model
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Network Model
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Congestion Traffic Minimization
Module Description
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Client Model
A client is an application or system that accesses a remote service on another computer system, known as a server, by way of a network. The term was first applied to devices that were not capable of running their own stand-alone programs, but could interact with remote computers via a network. These dumb terminals were clients of the time-sharing mainframe computer
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Server model
In computing, a server is any combination of hardware or software designed to
provide services to clients. When used alone, the term typically refers to a computer which may be running a server operating system, but is commonly used to refer to any software or dedicated hardware capable of providing services.
Generally, the channel quality is time-varying. For the ser-AP association decision, a user performs multiple samplings of the channel quality, and only the signal attenuation that
results from long-term channel condition changes are utilized our load model can
accommodate various additive load definitions such as the number of users associated
with an AP. It can also deal with the multiplicative user load contributions.
Congestion Traffic Minimization
We provided two different approaches, a model-free and a model-based one. The model-free method works on a longer time-scale processing traces of traffic aggregates over a small time interval. Using an anomaly-free trace it derives an associated probability law. Then it processes current traffic and quantifies whether it conforms to this probability law. The model-based method constructs a Markov modulated model of anomaly-free traffic measurements and relies on large deviations asymptotics and decision theory results to compare this model to ongoing traffic activity. We presented a rigorous framework to identify traffic anomalies providing asymptotic thresholds for anomaly detection. In our experimental results the model-free approach showed a somewhat better performance than the model-based one. This may be due to the fact that the former gains from the aggregation over a time-bucket in addition to the fact that the latter one requires the estimation of more parameters, hence, it may introduce a larger modeling error. For future work, it would be interesting to analyze the robustness of the anomaly detection mechanism to various model parameters.
Since we monitor the detailed distributional characteristics of traffic and do not rely on the mean or the first few moments we are confident that our approach can be successful against new types of (emerging) temporal and spatial anomalies.
Our method is of low implementation complexity (only an additional counter is required), and is based on first principles, so it would be interesting to investigate how it can be embedded on routers or other network devices.
REFERENCE:
Congestion Traffic Minimization
We provided two different approaches, a model-free and a model-based one. The model-free method works on a longer time-scale processing traces of traffic aggregates over a small time interval. Using an anomaly-free trace it derives an associated probability law. Then it processes current traffic and quantifies whether it conforms to this probability law. The model-based method constructs a Markov modulated model of anomaly-free traffic measurements and relies on large deviations asymptotics and decision theory results to compare this model to ongoing traffic activity. We presented a rigorous framework to identify traffic anomalies providing asymptotic thresholds for anomaly detection. In our experimental results the model-free approach showed a somewhat better performance than the model-based one. This may be due to the fact that the former gains from the aggregation over a time-bucket in addition to the fact that the latter one requires the estimation of more parameters, hence, it may introduce a larger modeling error. For future work, it would be interesting to analyze the robustness of the anomaly detection mechanism to various model parameters.
Since we monitor the detailed distributional characteristics of traffic and do not rely on the mean or the first few moments we are confident that our approach can be successful against new types of (emerging) temporal and spatial anomalies.
Our method is of low implementation complexity (only an additional counter is required), and is based on first principles, so it would be interesting to investigate how it can be embedded on routers or other network devices.
REFERENCE:
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